






'''simple docstring'''







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
  from PIL import Image




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     (
      '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
      '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
      '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
  )
  _a  : int      			=     '''CIDAS/clipseg-rd64-refined'''
  _a  : Tuple      			=     '''image_segmenter'''
  _a  : Optional[Any]      			=     CLIPSegForImageSegmentation

  _a  : Optional[Any]      			=     ['''image''', '''text''']
  _a  : int      			=     ['''image''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['vision']	)
         super().__init__(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.pre_processor(text=[label]   ,	images=[image]   ,	padding=UpperCamelCase__   ,	return_tensors='pt'	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         with torch.no_grad():
                a_						     =			self.model(**UpperCamelCase__	).logits
         return logits







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			outputs.cpu().detach().numpy()
         a_						     =			0
         a_						     =			1
         return Image.fromarray((array * 255).astype(np.uinta	)	)












'''simple docstring'''







import os
import sys
import unittest


__lowerCAmelCase        =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


__lowerCAmelCase        =							os.path.join("tests", "models", "bert", "test_modeling_bert.py")
__lowerCAmelCase        =							os.path.join("tests", "models", "blip", "test_modeling_blip.py")




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_test_to_tester_mapping(UpperCamelCase__	)
         a_						     =			get_test_to_tester_mapping(UpperCamelCase__	)

         a_						     =			{'BertModelTest': 'BertModelTester'}

         a_						     =			{
             'BlipModelTest': 'BlipModelTester',
             'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
             'BlipTextModelTest': 'BlipTextModelTester',
             'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
             'BlipVQAModelTest': 'BlipVQAModelTester',
             'BlipVisionModelTest': 'BlipVisionModelTester',
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_model_to_test_mapping(UpperCamelCase__	)
         a_						     =			get_model_to_test_mapping(UpperCamelCase__	)

         a_						     =			{
             'BertForMaskedLM': ['BertModelTest'],
             'BertForMultipleChoice': ['BertModelTest'],
             'BertForNextSentencePrediction': ['BertModelTest'],
             'BertForPreTraining': ['BertModelTest'],
             'BertForQuestionAnswering': ['BertModelTest'],
             'BertForSequenceClassification': ['BertModelTest'],
             'BertForTokenClassification': ['BertModelTest'],
             'BertLMHeadModel': ['BertModelTest'],
             'BertModel': ['BertModelTest'],
         }

         a_						     =			{
             'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
             'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
             'BlipForQuestionAnswering': ['BlipVQAModelTest'],
             'BlipModel': ['BlipModelTest'],
             'BlipTextModel': ['BlipTextModelTest'],
             'BlipVisionModel': ['BlipVisionModelTest'],
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_model_to_tester_mapping(UpperCamelCase__	)
         a_						     =			get_model_to_tester_mapping(UpperCamelCase__	)

         a_						     =			{
             'BertForMaskedLM': ['BertModelTester'],
             'BertForMultipleChoice': ['BertModelTester'],
             'BertForNextSentencePrediction': ['BertModelTester'],
             'BertForPreTraining': ['BertModelTester'],
             'BertForQuestionAnswering': ['BertModelTester'],
             'BertForSequenceClassification': ['BertModelTester'],
             'BertForTokenClassification': ['BertModelTester'],
             'BertLMHeadModel': ['BertModelTester'],
             'BertModel': ['BertModelTester'],
         }

         a_						     =			{
             'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
             'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
             'BlipForQuestionAnswering': ['BlipVQAModelTester'],
             'BlipModel': ['BlipModelTester'],
             'BlipTextModel': ['BlipTextModelTester'],
             'BlipVisionModel': ['BlipVisionModelTester'],
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)




